Recombination operator

For recombination two parents are randomly selected. The discrete recombination operator (d) generates an offspring by randomly taking the offspring s object parameters from one of the selected parents with equal probability. The intermediate recombination operator (i) takes the arithmetic mean of both parents parameters. The recombination can be omitted (—). 

Since each batch ID must occur a certain number of times (as often as the number of recipe steps of the batch), a recombination operator that preserves permutations is applied. The two best individuals out of a set of randomly chosen individuals are selected from the population. Then a random segment is copied from the first individual into a new individual. The missing elements are filled with the entries of the second individual. The newly generated individual is then subject to mutation with a probability p. By mutation, two randomly chosen entries are exchanged. 

Figure 7.10 Principle of a single manifold split-and-recombination operation...

When two solutions enter the micromixer, the number of segments is doubled in one split-and-recombination operation. Therefore, in this method, the number of solution segments is increased exponentially by repeating the operation sequentially along the channel (Figure 7.11). After the nth split-and-recombination iteration, the number of segments increases to This operation decreases the diffusion path significantly... 

The Yamatake YM-1 micromixer is another type of split-and-recombination micromixer (Figure 7.13). As shown in Figure 7.13, the two solutions to be mixed are distributed into many streams in the first part of the mixer. Then, several split-and-recombination operations are performed sequentially to increase the number of segments, leading to fast mixing because of the short diffusion path. 

Figure 7.11 Principle of multiple manifold split-and-recombination operations...

Heuristic genetic algorithms have been applied to job shop scheduling. In these genetic schemes, problem specific heuristics are incorporated in the recombination operators (such as optimization operators based). 

Pair up the individuals in the mating pool and generate A(> /r) new-born offspring individuals using the operators of recombination and mutation. In this study, each chromosome consists of three portions. For the first portion of the chromosome, discrete recombination operators, repeated exchange mutation operators, and evolutionary inversion mutation operators are employed. For the second portion of the chromosome, traditional gene-alter mutation operators and traditional discrete recombination operators are developed. For the third portion of the chromosome, exchange mutation operators and traditional discrete recombination operators are developed. 

The discrete recombination operator was used in this study. The procedure of the discrete recombination operator for the first portion of the chromosome is presented on the next page ... 

The other two methods exploit the fact that the genes are real-valued. The first of these, the intermediate recombination operator, determines the value of the child s genes by averaging the two values in the parents genes. The second method, the random intermediate recombinator, probabilistically determines the evenness of the contribution of each parent for each parameter. 

ABSTRACT. Crystal structures of many complex sulfides can be obtained by recombination of fragments of archetypal structures by the action of various structure-building operators. Chemical categories, coordination polyhedra, archetypes and recombination operators for these structures are described in the paper. These structures often occur as members of accretional or variable-fit homologous series or as homologous pairs of various kinds. Three case studies supplemented by exercises are described in some detail the lillianite homologous series, the misfit layer structures and those structures that represent combination of the accretional and variable-fit principles. 

The PSO algorithm is based on a sociometric principle called gi,, which connects all the members of the swarm to one another (Kennedy and Eberhart, 2001). Each particle is affected by the very best performance of any member of the whole population. The key operators involved in evolutionary algorithm (EA) are recombination, mutation and selection however, PSO does not have a direct recombination operator (Kennedy and Eberhart, 2001). But, the stochastic acceleration of a particle towards its previous best position as well as towards the best particle of the swarm resembles the recombination in EA. In PSO, the information exchange takes place merely among the particle s own experience and the experience of the best particle in the swarm instead of being carried from fitness-dependent... 

No recombination operator combining features of different individuals is used with EP. In EP, individuals are viewed as species, and distinct species do not recombine. 

Genetic algorithms start by creating a population of models. Recombination operations between pairs of parent models occur more frequently for those that have a high fitness score. If the fitness score of a newly generated model is... 

Abstract. The intramolecular gene assembly model, [1], uses three molecular recombination operations Id, diad, and hi. A computing model with two contextual recombination operations del and tri, which axe based on Id and dIad, respectively, is considered in [6] and its computational power is investigated. In the present paper, we expand the computing model with a new molecular operation such as cpy - copy. Then we prove that the extended contextual intramolecular gene assembly model is both computationally universal and efficient. 

A particularly interesting feature of the system is that the recombination operations are applied in a maximally parallel manner. 

A uniform crossover is the generalized form of crossover where chromosomal exchanges happen between parents, across multiple (the number chosen randomly) cut-points. The recombination operator has a probability associated with it which dictates how often it is used. The probability of crossover is typically set to a high value (around 99%) for binary coded representation. A random number is drawn and whenever it falls below the crossover probability, two individuals (selected using one of the selection schemes described in the previous section) are allowed to undergo crossover. If the random number test fails, the chosen individuals are duplicated and placed in the offspring population. 

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